Intercept Estimation in Nonlinear Selection Models

41 Pages Posted: 22 May 2021

See all articles by Wiji Arulampalam

Wiji Arulampalam

University of Warwick - Department of Economics; IZA Institute of Labor Economics

Valentina Corradi

University of Surrey - School of Economics

Daniel Gutknecht

Goethe University Frankfurt

Abstract

We propose various semiparametric estimators for nonlinear selection models, where slope and intercept can be separately identifed. When the selection equation satisfies a monotonic index restriction, we suggest a local polynomial estimator, using only observations for which the marginal distribution of instrument index is close to one. Such an estimator achieves a univariate nonparametric rate, which can range from a cubic to an 'almost' parametric rate. We then consider the case in which either the monotonic index restriction does not hold and/ or the set of observations with propensity score close to one is thin so that convergence occurs at most at a cubic rate. We explore the finite sample behaviour in a Monte Carlo study, and illustrate the use of our estimator using a model for count data with multiplicative unobserved heterogeneity.

JEL Classification: C14, C21, C24

Suggested Citation

Arulampalam, Wiji and Corradi, Valentina and Gutknecht, Daniel, Intercept Estimation in Nonlinear Selection Models. IZA Discussion Paper No. 14364, Available at SSRN: https://ssrn.com/abstract=3851038 or http://dx.doi.org/10.2139/ssrn.3851038

Wiji Arulampalam (Contact Author)

University of Warwick - Department of Economics ( email )

Coventry CV4 7AL
United Kingdom
01203 523471 (Phone)
01203 523032 (Fax)

IZA Institute of Labor Economics

P.O. Box 7240
Bonn, D-53072
Germany

Valentina Corradi

University of Surrey - School of Economics ( email )

Guildford
Guildford, Surrey GU2 5XH
United Kingdom

Daniel Gutknecht

Goethe University Frankfurt ( email )

Frankfurt am Main, 60629
Germany

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